CN110941486A - Task management method and device, electronic equipment and computer readable storage medium - Google Patents
Task management method and device, electronic equipment and computer readable storage medium Download PDFInfo
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- CN110941486A CN110941486A CN201911176593.6A CN201911176593A CN110941486A CN 110941486 A CN110941486 A CN 110941486A CN 201911176593 A CN201911176593 A CN 201911176593A CN 110941486 A CN110941486 A CN 110941486A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
Abstract
The invention discloses a task management method and device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: setting task information of a task to be executed; inputting the task information and the cluster resource information into a pre-trained task allocation model, and outputting a task allocation result through the task allocation model; and distributing the task to be executed to the node indicated by the task distribution result for execution. By the technical scheme, the automation and convenience degree of task management are improved, and the development and maintenance cost is reduced.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a task management method and apparatus, an electronic device, and a computer-readable storage medium.
Background
Currently, a task scheduling component is generally used in an operating system to allocate tasks. Common task scheduling components are Quartz, Azkaban, and the like.
Quartz is an open-source task scheduling component, supports distributed scheduling and task persistence, however, Quartz does not support process scheduling, cannot be applied to scheduling scenarios with a sequential dependency relationship between tasks, and requires developers to perform supplementary development and configuration when encountering the process scheduling scenarios.
Azkaban is a workflow task scheduling component, supports process scheduling, and can run a group of works and processes in a workflow in a specific order. However, Azkaban can only be integrated in a common Hadoop ecological big data component, personalized support for other general tasks is lacked, and a developer is still required to perform supplementary configuration on the Azkaban.
Therefore, the task allocation mode in the related art has a single application range, lacks of expandability and compatibility, and consumes a large amount of development cost in the expansion process.
Therefore, how to provide a task allocation method with strong compatibility becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The invention provides a task management method and device, electronic equipment and a computer readable storage medium, and provides a task allocation mode with strong compatibility aiming at the technical problem that the task allocation mode in the related technology lacks expandability and compatibility, so that the task management can be automatically carried out, and the development cost is greatly reduced.
A first aspect of the present invention provides a task management method, including: setting task information of a task to be executed; inputting the task information and the cluster resource information into a pre-trained task allocation model, and outputting a task allocation result through the task allocation model; and distributing the task to be executed to the node indicated by the task distribution result for execution.
In the above embodiment of the present invention, optionally, before the step of setting the task information of the task to be executed, the method further includes: acquiring a training sample set of the task allocation model, wherein each training sample in the training sample set comprises historical task information of a historical task, historical cluster resource information before the historical task is executed and a historical node for executing the historical task; initializing model parameters of an initial task allocation model; inputting the historical task information and the historical cluster resource information of each training sample into the initial task allocation model to obtain a prediction node corresponding to each training sample; and adjusting model parameters of the initial task allocation model based on the difference between the prediction node and the historical node of each training sample to obtain the task allocation model.
In the above embodiment of the present invention, optionally, the method further includes: in the process that the node indicated by the task allocation result executes the task to be executed, task state information is acquired at regular time; and visually displaying the task state information.
In the above embodiment of the present invention, optionally, the method further includes: detecting whether the task state information completely meets a preset safety condition; on the basis of the condition that the task state information does not completely meet the preset safety condition, identifying a part of the task state information which does not meet the preset safety condition as abnormal information; and sending alarm information aiming at the abnormal information.
In the above embodiment of the present invention, optionally, the method further includes: storing the alarm information to the cluster resource information; adding the cluster resource information in which the alarm information is stored into a training sample set of the task allocation model to obtain an updated training sample set; and updating the task allocation model according to the updated training sample set.
In the above embodiment of the present invention, optionally, before the step of setting the task information of the task to be executed, the method further includes: according to a task creating instruction, creating the task to be executed; acquiring a task information setting instruction for the task to be executed; the step of setting task information of the task to be executed specifically includes: and setting the task information for the task to be executed according to the task information setting instruction aiming at the task to be executed.
A second aspect of the present invention provides a task management apparatus, including: the task information setting unit is used for setting task information of a task to be executed; the task allocation computing unit inputs the task information and the cluster resource information into a pre-trained task allocation model and outputs a task allocation result through the task allocation model; and the task allocation unit is used for allocating the task to be executed to the node indicated by the task allocation result for execution.
In the above embodiment of the present invention, optionally, the method further includes: a training sample set obtaining unit, configured to obtain a training sample set of the task allocation model before the task information setting unit sets the task information, where each training sample in the training sample set includes historical task information of a historical task, historical cluster resource information before the historical task is executed, and a historical node for executing the historical task; the parameter initialization unit is used for initializing the model parameters of the initial task allocation model; the model training unit is used for inputting the historical task information and the historical cluster resource information of each training sample into the initial task allocation model to obtain a prediction node corresponding to each training sample; and the parameter adjusting unit is used for adjusting model parameters of the initial task allocation model based on the difference between the prediction node and the historical node of each training sample to obtain the task allocation model.
In the above embodiment of the present invention, optionally, the method further includes: a state information obtaining unit, configured to obtain task state information at regular time in a process in which the node indicated by the task allocation result executes the task to be executed; and the state information display unit is used for visually displaying the task state information.
In the above embodiment of the present invention, optionally, the method further includes: the state information detection unit is used for detecting whether the task state information completely meets a preset safety condition; the abnormal information identification unit is used for identifying a part which does not meet the preset safety condition in the task state information as abnormal information under the condition that all the task state information does not meet the preset safety condition; and the alarm unit is used for sending alarm information aiming at the abnormal information.
In the above embodiment of the present invention, optionally, the method further includes: the alarm information storage unit is used for storing the alarm information to the cluster resource information; the training sample updating unit is used for adding the cluster resource information in which the alarm information is stored into a training sample set of the task allocation model to obtain an updated training sample set; and the model updating unit is used for updating the task allocation model according to the updated training sample set.
In the above embodiment of the present invention, optionally, the method further includes: the task creating unit is used for creating the task to be executed according to a task creating instruction before the task information is set by the task information setting unit; a setting instruction obtaining unit, configured to obtain a task information setting instruction for the task to be executed; the task information setting unit is specifically configured to set the task information for the task to be executed according to the task information setting instruction for the task to be executed.
A third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the first and second aspects above.
A fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for performing the method flow of any one of the first and second aspects described above.
According to the technical scheme, the task allocation method with high compatibility is provided for the technical problem that the task allocation method in the related technology is lack of expandability and compatibility, the task allocation method can automatically allocate the tasks according to the task information and the historical rule of the task allocation result, and the automation and convenience degree of task management are improved.
Specifically, firstly, a user creates a task to be executed at a client, sets task information including task type, task content, execution time and the like for the created task to be executed, and then inputs the task information and real-time cluster resource information in a current scene into a pre-trained task allocation model. The task allocation model is obtained by performing machine learning in advance, the input of the training sample is historical task information and historical cluster resource information under the current scene, and the output is a task allocation result corresponding to the historical task information. Therefore, the task allocation model is obtained by training and learning the association relation between the task information and the task allocation result by taking the task information and the cluster resource information of the scene as conditions. Therefore, at present, the task information and the real-time cluster resource information in the current scene are input into the pre-trained task allocation model, and the task allocation result of the task information can be obtained through corresponding calculation according to the incidence relation.
Because the task allocation result includes the node, the task content, the execution time, and the like for executing the task, the task to be executed may be allocated to the node for executing the task, so that the node executes the task content specified by the task allocation result at the execution time specified by the task allocation result.
In the related art, no matter which task scheduling component is adopted, each time an unsupported task scheduling scenario is encountered, a developer needs to perform further complementary development and configuration on the task scheduling component so that the task scheduling component can be suitable for the task scheduling scenario. According to the technical scheme, the task allocation model is used for automatically selecting the nodes for the tasks to execute in a machine learning mode, various historical task information and historical cluster resource information are used as input training, and all task scheduling scenes are included, so that manual supplementary development and configuration are not needed, the labor cost and the time cost required by task management are greatly reduced, the error problem caused by manual supplementary development and configuration is avoided, the task management can be automatically carried out by developers, the automation and convenience degree of the task management is greatly improved, and the development and maintenance cost is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 shows a flow diagram of a task management method according to one embodiment of the invention;
FIG. 2 illustrates a flow diagram of training a task assignment model according to one embodiment of the invention;
FIG. 3 shows a flow diagram of a task management method according to another embodiment of the invention;
FIG. 4 shows a schematic diagram of a task management process according to one embodiment of the invention;
FIG. 5 shows a block diagram of a task management device according to one embodiment of the invention;
FIG. 6 shows a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the related art, no matter which task scheduling component is adopted, each time an unsupported task scheduling scenario is encountered, a developer needs to perform further complementary development and configuration on the task scheduling component so that the task scheduling component can be suitable for the task scheduling scenario.
According to the technical scheme, the task allocation model is used for automatically selecting the nodes for the task to execute in a machine learning mode, various historical task information and historical cluster resource information are used as input training, all task scheduling scenes are included, and therefore manual supplementary development and configuration are not needed.
The technical solution of the present invention is analyzed in detail by the following embodiments.
FIG. 1 shows a flow diagram of a task management method according to one embodiment of the invention.
As shown in fig. 1, a task management method according to an embodiment of the present invention includes:
The user establishes a task to be executed at the client side, and sets task information including task type, task content, execution time and the like for the established task to be executed.
And 104, inputting the task information and the cluster resource information into a pre-trained task allocation model, and outputting a task allocation result through the task allocation model.
The task allocation model is obtained by performing machine learning in advance, the input of the training sample is historical task information and historical cluster resource information under the current scene, and the output is a task allocation result corresponding to the historical task information. Therefore, the task allocation model is obtained by training and learning the association relation between the task information and the task allocation result by taking the task information and the cluster resource information of the scene as conditions. Therefore, at present, the task information and the real-time cluster resource information in the current scene are input into the pre-trained task allocation model, and the task allocation result of the task information can be obtained through corresponding calculation according to the incidence relation.
And 106, distributing the task to be executed to the node indicated by the task distribution result for execution.
Because the task allocation result includes the node, the task content, the execution time, and the like for executing the task, the task to be executed may be allocated to the node for executing the task, so that the node executes the task content specified by the task allocation result at the execution time specified by the task allocation result.
In summary, for the technical problem that the task allocation method in the related art lacks scalability and compatibility, a task allocation method with strong compatibility is provided, and the task allocation method can automatically allocate tasks according to the task information and the historical rule of the task allocation result. Therefore, manual supplement development and configuration are not needed, labor cost and time cost required by task management are greatly reduced, the error problem easily caused by manual supplement development and configuration is avoided, the task management can be automatically carried out by developers, the automation and convenience degree of the task management is greatly improved, and the development and maintenance cost is reduced.
FIG. 2 shows a flow diagram for training a task assignment model, according to one embodiment of the invention.
As shown in fig. 2, a task management method according to another embodiment of the present invention includes:
Before creating a task to be executed, a pre-trained task allocation model needs to be obtained first, so that a task allocation result is directly output through the task allocation model.
The task allocation model is obtained by machine learning, the input of the training sample is historical task information and historical cluster resource information under the current scene, and the output is a task allocation result corresponding to the historical task information. The task allocation result corresponding to the historical task information is a task allocation result which is obtained according to the historical task information and historical cluster resource information under the current scene and is suitable for the historical task information, therefore, a large amount of reasonable historical information is used as an input sample of training to carry out next-step machine learning, and a reliable basis can be established for establishing a practical and effective model.
The machine learning method includes, but is not limited to, a decision tree algorithm, a naive Bayes algorithm, a random forest algorithm, a neural network algorithm, an association rule algorithm, an expectation maximization algorithm and deep learning. The technical scheme of the invention can adopt any mode or the combined training of any multiple modes to obtain the final task allocation model.
And 208, adjusting model parameters of the initial task allocation model based on the difference between the prediction node and the historical node of each training sample to obtain a task allocation model.
Each machine learning method has a universal initial task allocation model, and can be used for inputting training samples of a sample set into the initial task allocation model to obtain a prediction result, and then comparing the prediction result with a historical result corresponding to the training samples, so that the initial task allocation model is gradually corrected. According to the technical scheme, historical task information and historical cluster resource information of each training sample are input into an initial task allocation model to obtain a prediction node corresponding to each training sample, whether the prediction node is consistent with the historical node corresponding to the training sample or not is judged, if not, model parameters of the initial task allocation model are adjusted until the prediction node is consistent with the historical node corresponding to the training sample.
And because the number of training samples is huge, model parameters of the initial task allocation model can be continuously corrected based on the training samples until a reasonable task allocation model is obtained.
Therefore, the task information and the real-time cluster resource information under the current scene are input into the pre-trained task allocation model, the task allocation result of the task information can be correspondingly calculated according to the incidence relation, manual supplement development and configuration are not needed, the labor cost and the time cost required by task management are greatly reduced, the error problem easily caused by manual supplement development and configuration is avoided, the task management can be automatically carried out by developers, the automation and convenience degree of the task management is greatly improved, and the development and maintenance cost is reduced.
Fig. 3 shows a flowchart of a task management method according to another embodiment of the invention.
As shown in fig. 3, a task management method according to another embodiment of the present invention includes:
The user can carry out task creation operation at the client, and correspondingly, the system can receive the task creation instruction and create a corresponding task to be executed.
And 304, acquiring a task information setting instruction for the task to be executed.
Then, the user can set task information including task type, task content, execution time and the like for the newly-built task to be executed at the client.
And 308, inputting the task information and the cluster resource information into a pre-trained task allocation model, and outputting a task allocation result through the task allocation model.
The task allocation model is obtained by performing machine learning in advance, the input of the training sample is historical task information and historical cluster resource information under the current scene, and the output is a task allocation result corresponding to the historical task information. Therefore, the task allocation model is obtained by training and learning the association relation between the task information and the task allocation result by taking the task information and the cluster resource information of the scene as conditions. Therefore, at present, the task information and the real-time cluster resource information in the current scene are input into the pre-trained task allocation model, and the task allocation result of the task information can be obtained through corresponding calculation according to the incidence relation.
And 310, distributing the task to be executed to the node indicated by the task distribution result for execution.
Because the task allocation result includes the node, the task content, the execution time, and the like for executing the task, the task to be executed may be allocated to the node for executing the task, so that the node executes the task content specified by the task allocation result at the execution time specified by the task allocation result.
Step 312, in the process that the node indicated by the task allocation result executes the task to be executed, task state information is obtained at regular time.
In the technical scheme of the invention, a monitoring system is additionally arranged to monitor the task state information in the task execution process of the node, wherein the task state information comprises the working state of the node, the busy degree of the node, the task execution speed, the executed content of the task, the percentage of the task execution amount in the total task amount and the like. In one possible design, the task state information is visually displayed, so that a user can conveniently make manual judgment according to the visual display content to identify whether the task is in the normal execution process.
And step 314, detecting whether all the task state information meets preset safety conditions.
Through the task state information, whether the task is in the normal execution process can be judged. For example, a predetermined safety condition such as a designated threshold or a designated state can be set for each kind of task state information, and the system can automatically determine whether each kind of task state information reaches the designated threshold or the designated state, if so, it indicates that the task is in a normal execution process, and if not, it indicates that the task is executed or a problem occurs in the node itself.
And step 316, identifying a part of the task state information which does not meet the preset safety condition as abnormal information under the condition that all the task state information does not meet the preset safety condition.
And step 318, sending out alarm information aiming at the abnormal information.
And identifying the part of the task state information which does not reach the preset safety condition as abnormal information, and giving an alarm to the abnormal information. The issuing mode of the warning information includes but is not limited to visual display, audio prompt, short message prompt, vibration prompt and the like.
And step 320, storing the alarm information to the cluster resource information.
The alarm information that has occurred is also part of the current scenario and therefore may be stored as part of the cluster resource information.
The current cluster resource information is updated and also has corresponding tasks and task execution nodes, so that the current cluster resource information can be used as an effective historical training sample. Therefore, the training sample set can be stored in the training sample set, and the updating of the training sample set is realized.
And finally, retraining the task allocation model according to the updated training sample set so as to realize the correction of the task allocation model according to the actual situation, so that the task allocation model can always include the characteristics of the latest training sample.
In conclusion, the cluster resource information and even the task allocation model can be synchronously updated according to the alarm information, so that the task allocation model can always learn the characteristics of all the generated training samples, and the practicability and effectiveness of the task allocation model are improved.
Referring to fig. 4, a user creates a task in a task management system, the task management system stores the task in a storage system, and then the task management system reads a task allocation model in a machine learning system and outputs a node for executing the task through the task allocation model.
The node that executes the task is then sent to the task scheduler, which schedules the task executor to execute the task at that node. In the process that the node executes the task, the monitoring system monitors the task, and when abnormal information is monitored, the abnormal information is sent to the warning system, and the warning system gives a warning to the user.
The task management system is mainly used for managing tasks of a user, and has the functions of creating, submitting, canceling, inquiring and the like. The storage system is mainly used for storing data such as user information, task information, execution history information, alarm information and the like, on one hand, provides user query history, and on the other hand, provides training data for a machine learning system model. The machine learning system is mainly used for reading historical task scheduling information and the current cluster resource use condition and training the model in a targeted manner. And making a self-learning process of a scheduling decision result according to the task information in the message queue by the model. The task scheduler is mainly used for actually distributing the tasks to the task executors in the work clusters according to decision results generated by the machine learning system. The task executor is mainly used for receiving and executing tasks sent by the task scheduler and reporting task state information to the monitoring system at regular time. The monitoring system is mainly used for visually displaying the task execution condition to the user, and the user can conveniently track the progress of the task. The alarm system is mainly used for associating the alarm with the user and tracking the execution result of the task, so that the user can know the task at the first time after the task is abnormally executed.
Specifically, the user creates a related task according to the need in the task management system, and after the task is created, the user can select the modes of immediate execution, timing execution and the like in the task management system to submit the task. The submitted task information is stored in a storage system, and the machine learning system carries out model training on historical information in the storage system, puts the currently received task and cluster resource information into a model for decision making, generates a result, and selects a specific node to which the task is distributed.
And the task scheduler distributes the tasks to corresponding nodes according to the result generated by the decision, the task executor starts to execute the distributed tasks and reports the task state information to the monitoring system at regular time. The monitoring system visually displays the received task information, and a user can conveniently check the running state condition of the task. And the alarm system sends an alarm to the user about the abnormal task information and stores the alarm information in the storage system to provide data for subsequent machine learning system training.
According to the technical scheme, the information related to the task is filled in the webpage, the task can be created without an additional programming basis, the running task is submitted immediately, the execution progress can be observed in real time on the webpage, and the operation and use process is simple and convenient. And training the submitted historical tasks by a machine learning method, improving the self-learning capability of the model aiming at the dimensionalities of user information, task types, cluster resources, scheduling time and the like in the historical tasks, reducing the identification cost of developers, quickly and accurately training the model by a mature machine learning algorithm, and intelligently selecting proper nodes to execute the tasks by the trained model, thereby improving the task scheduling efficiency on the whole cluster and the resource utilization rate. In addition, the real-time monitoring and abnormal alarming of submitted tasks can be completed by the integrated realization of monitoring, alarming and retrying mechanisms of the tasks, and the abnormal tasks (such as the tasks which fail to restart) are managed and tracked, so that the timeliness and the accuracy of the task operation success are improved, and the manual workload is reduced.
Fig. 5 shows a block diagram of a task management device according to an embodiment of the invention.
As shown in fig. 5, a task management device 500 according to an embodiment of the present invention includes: a task information setting unit 502, configured to set task information of a task to be executed; the task allocation calculation unit 504 is used for inputting the task information and the cluster resource information into a pre-trained task allocation model and outputting a task allocation result through the task allocation model; and a task allocation unit 506, configured to allocate the task to be executed to the node indicated by the task allocation result for execution.
In the above embodiment of the present invention, optionally, the method further includes: a training sample set obtaining unit, configured to obtain a training sample set of the task allocation model before the task information is set by the task information setting unit 502, where each training sample in the training sample set includes historical task information of a historical task, historical cluster resource information before the historical task is executed, and a historical node for executing the historical task; the parameter initialization unit is used for initializing the model parameters of the initial task allocation model; the model training unit is used for inputting the historical task information and the historical cluster resource information of each training sample into the initial task allocation model to obtain a prediction node corresponding to each training sample; and the parameter adjusting unit is used for adjusting model parameters of the initial task allocation model based on the difference between the prediction node and the historical node of each training sample to obtain the task allocation model.
In the above embodiment of the present invention, optionally, the method further includes: a state information obtaining unit, configured to obtain task state information at regular time in a process in which the node indicated by the task allocation result executes the task to be executed; and the state information display unit is used for visually displaying the task state information.
In the above embodiment of the present invention, optionally, the method further includes: the state information detection unit is used for detecting whether the task state information completely meets a preset safety condition; the abnormal information identification unit is used for identifying a part which does not meet the preset safety condition in the task state information as abnormal information under the condition that all the task state information does not meet the preset safety condition; and the alarm unit is used for sending alarm information aiming at the abnormal information.
In the above embodiment of the present invention, optionally, the method further includes: the alarm information storage unit is used for storing the alarm information to the cluster resource information; the training sample updating unit is used for adding the cluster resource information in which the alarm information is stored into a training sample set of the task allocation model to obtain an updated training sample set; and the model updating unit is used for updating the task allocation model according to the updated training sample set.
In the above embodiment of the present invention, optionally, the method further includes: a task creating unit, configured to create the task to be executed according to a task creating instruction before the task information setting unit 502 sets the task information; a setting instruction obtaining unit, configured to obtain a task information setting instruction for the task to be executed; the task information setting unit is specifically configured to set the task information for the task to be executed according to the task information setting instruction for the task to be executed.
The task management device 500 uses the scheme described in any one of the embodiments shown in fig. 1 to fig. 4, and therefore, all the technical effects described above are achieved, and are not described again here.
FIG. 6 shows a block diagram of an electronic device according to an embodiment of the invention.
As shown in FIG. 6, an electronic device 600 of one embodiment of the invention includes at least one memory 602; and a processor 604 communicatively coupled to the at least one memory 602; wherein the memory stores instructions executable by the at least one processor 604 and configured to perform the aspects of any of the embodiments of fig. 1-4 described above. Therefore, the electronic device 600 has the same technical effect as any one of the embodiments of fig. 1 to 4, and is not described herein again.
The electronic device of embodiments of the present invention exists in a variety of forms, including but not limited to:
(1) mobile communication devices, which are characterized by mobile communication capabilities and are primarily targeted at providing voice and data communications. Such terminals include smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) The ultra-mobile personal computer equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include PDA, MID, and UMPC devices, such as ipads.
(3) Portable entertainment devices such devices may display and play multimedia content. Such devices include audio and video players (e.g., ipods), handheld game consoles, electronic books, as well as smart toys and portable car navigation devices.
(4) The server is similar to a general computer architecture, but has higher requirements on processing capability, stability, reliability, safety, expandability, manageability and the like because of the need of providing highly reliable services.
(5) And other electronic devices with data interaction functions.
In addition, an embodiment of the present invention provides a computer-readable storage medium, which stores computer-executable instructions for performing the method flow described in any one of the above embodiments of fig. 1 to 4.
The technical scheme of the invention is described in detail in combination with the attached drawings, and through the technical scheme of the invention, manual supplementary development and configuration are not needed, so that the labor cost and the time cost required by task management are greatly reduced, the error problem easily caused by manual supplementary development and configuration is avoided, the task management can be automatically carried out by developers, the automation and the convenience degree of the task management are greatly improved, and the development and maintenance cost is reduced.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for task management, comprising:
setting task information of a task to be executed;
inputting the task information and the cluster resource information into a pre-trained task allocation model, and outputting a task allocation result through the task allocation model;
and distributing the task to be executed to the node indicated by the task distribution result for execution.
2. The task management method according to claim 1, further comprising, before the step of setting task information of a task to be executed:
acquiring a training sample set of the task allocation model, wherein each training sample in the training sample set comprises historical task information of a historical task, historical cluster resource information before the historical task is executed and a historical node for executing the historical task;
initializing model parameters of an initial task allocation model;
inputting the historical task information and the historical cluster resource information of each training sample into the initial task allocation model to obtain a prediction node corresponding to each training sample;
and adjusting model parameters of the initial task allocation model based on the difference between the prediction node and the historical node of each training sample to obtain the task allocation model.
3. The task management method according to claim 2, further comprising:
in the process that the node indicated by the task allocation result executes the task to be executed, task state information is acquired at regular time;
and visually displaying the task state information.
4. The task management method according to claim 3, further comprising:
detecting whether the task state information completely meets a preset safety condition;
on the basis of the condition that the task state information does not completely meet the preset safety condition, identifying a part of the task state information which does not meet the preset safety condition as abnormal information;
and sending alarm information aiming at the abnormal information.
5. The task management method according to claim 4, further comprising:
storing the alarm information to the cluster resource information;
adding the cluster resource information in which the alarm information is stored into a training sample set of the task allocation model to obtain an updated training sample set;
and updating the task allocation model according to the updated training sample set.
6. The task management method according to any one of claims 1 to 5, further comprising, before the step of setting task information of a task to be executed:
according to a task creating instruction, creating the task to be executed;
acquiring a task information setting instruction for the task to be executed;
the step of setting task information of the task to be executed specifically includes:
and setting the task information for the task to be executed according to the task information setting instruction aiming at the task to be executed.
7. A task management apparatus, comprising:
the task information setting unit is used for setting task information of a task to be executed;
the task allocation computing unit inputs the task information and the cluster resource information into a pre-trained task allocation model and outputs a task allocation result through the task allocation model;
and the task allocation unit is used for allocating the task to be executed to the node indicated by the task allocation result for execution.
8. The task management apparatus according to claim 7, further comprising:
a training sample set obtaining unit, configured to obtain a training sample set of the task allocation model before the task information setting unit sets the task information, where each training sample in the training sample set includes historical task information of a historical task, historical cluster resource information before the historical task is executed, and a historical node for executing the historical task;
the parameter initialization unit is used for initializing the model parameters of the initial task allocation model;
the model training unit is used for inputting the historical task information and the historical cluster resource information of each training sample into the initial task allocation model to obtain a prediction node corresponding to each training sample;
and the parameter adjusting unit is used for adjusting model parameters of the initial task allocation model based on the difference between the prediction node and the historical node of each training sample to obtain the task allocation model.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1 to 6.
10. A computer-readable storage medium having stored thereon computer-executable instructions for performing the method flow of any of claims 1-6.
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| CN201911176593.6A CN110941486A (en) | 2019-11-26 | 2019-11-26 | Task management method and device, electronic equipment and computer readable storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113342535A (en) * | 2021-06-30 | 2021-09-03 | 中国工商银行股份有限公司 | Task data distribution method and device |
| CN113344383A (en) * | 2021-06-04 | 2021-09-03 | 兰州理工大学 | Energy-saving workshop scheduling system for distributed heterogeneous factory |
| CN114358649A (en) * | 2022-01-17 | 2022-04-15 | 安徽君鲲科技有限公司 | Maritime affair site supervision method and system |
-
2019
- 2019-11-26 CN CN201911176593.6A patent/CN110941486A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344383A (en) * | 2021-06-04 | 2021-09-03 | 兰州理工大学 | Energy-saving workshop scheduling system for distributed heterogeneous factory |
| CN113342535A (en) * | 2021-06-30 | 2021-09-03 | 中国工商银行股份有限公司 | Task data distribution method and device |
| CN114358649A (en) * | 2022-01-17 | 2022-04-15 | 安徽君鲲科技有限公司 | Maritime affair site supervision method and system |
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